Field of the Invention
The present invention relates to artificial intelligence systems used to analyze participants engaged in a sport and, in particular, to characterizing motion patterns of one or more agents from spatiotemporal data.
Description of the Related Art
Automated systems have been deployed that can analyze the plan and tactics of players of certain sports, e.g., tennis. In such cases, a state of the game is updated regularly at short time intervals, such as when a point is scored. Over time, such a system may build a library of successful plans (resulting in winning a point) and unsuccessful plans (resulting in losing a point or losing service) for a specific player. Such a system may then identify a set of proven plans or tactics from the library that characterize a player, given the relative strengths and weaknesses of the player's plans.
One shortcoming is that these systems rely on the game being segmented into short discretized plays, such as the points of a tennis match. However, some sporting events or activities, such as soccer matches and hockey games, proceed in a low-scoring, continuous fashion, without an easy way to segment play into short, identifiable, discrete segments such as points or plays. In such cases, the system has difficulty in building a library of successful and unsuccessful plans that characterize a player's or team's behavior.
An additional issue for such systems is that tracking data may not be available for each player or participant, particularly for team sports with multiple players or agents on each team. Because player tracking data is costly and difficult to obtain, only partial team tracing data may be available, often in the form of ball tracking. However, player movement data may not be available.